It's cool that it works, but this isn't entirely surprising result. Compression can be understood as a combination of probability modelling and entropy coding.

It's cool that it works, but this isn't entirely surprising result. Compression can be understood as a combination of probability modelling and entropy coding. Entropy coding takes a predicted probability of the next symbol (symbol can be a word, a color, audio sample, etc.) and converts it into bits. This is a solved problem. We know how to code this with perfect efficiency (however PNG didn't get optimal arithmetic coding due to patents, so its Huffman coding is optimal only for probabilities proportional to powers of two). So the rest of the lossless compression is the art of predicting what will come next based on data decompressed so far. Standard compression algorithms have simple hardcoded rules for this, mostly predicting exact repetitions of data seen recently. OTOH ML models have seen many more patterns, can express probabilities with more finesse, and setups like LLMs are very good at adapting based on previous data.